High-quality Low-dose CT Reconstruction Using Convolutional Neural Networks with Spatial and Channel Squeeze and Excitation

TL;DR

This paper introduces HQINet, a convolutional neural network designed to enhance low-dose CT images by effectively reconstructing high-quality images from reduced radiation data, improving diagnostic accuracy.

Contribution

The paper proposes a novel encoder-decoder CNN architecture with spatial and channel squeeze-and-excitation modules for improved low-dose CT reconstruction.

Findings

Achieved a 5.5dB increase in PSNR

Improved mutual information by 0.29

Demonstrated superior image quality on real low-dose CT data

Abstract

Low-dose computed tomography (CT) allows the reduction of radiation risk in clinical applications at the expense of image quality, which deteriorates the diagnosis accuracy of radiologists. In this work, we present a High-Quality Imaging network (HQINet) for the CT image reconstruction from Low-dose computed tomography (CT) acquisitions. HQINet was a convolutional encoder-decoder architecture, where the encoder was used to extract spatial and temporal information from three contiguous slices while the decoder was used to recover the spacial information of the middle slice. We provide experimental results on the real projection data from low-dose CT Image and Projection Data (LDCT-and-Projection-data), demonstrating that the proposed approach yielded a notable improvement of the performance in terms of image quality, with a rise of 5.5dB in terms of peak signal-to-noise ratio (PSNR) and 0.29 in terms of mutual information (MI).